Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Joint angle variability in 3D bimanual pointing: uncontrolled manifold analysis.

Dmitry Domkin1, Jozsef Laczko, Mats Djupsjöbacka

  • 1Centre for Musculoskeletal Research, University of Gävle, Box 7629, 907 12, Umeå, Sweden. dmitry.domkin@hig.se

Experimental Brain Research
|January 26, 2005
PubMed
Summary

The uncontrolled manifold (UCM) approach reveals the central nervous system (CNS) prioritizes stabilizing relative arm trajectories over individual arm paths during bimanual movements. Practice improves performance without altering this stabilization strategy.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

FES-Cycling at the Cybathlon 2024: assessing accuracy of power-pedals for low power output in a virtual race scenario.

Journal of neuroengineering and rehabilitation·2026
Same author

Whole-body pointing as superposition of two multi-joint components.

Experimental brain research·2026
Same author

Functional changes in spinal circuitry in essential tremor revealed with analysis of intramuscle synergies.

Journal of neurophysiology·2026
Same author

Assessment of the influence of lower limb cycling on arm muscle coordination during upper limb cycling: a pilot study.

Journal of neuroengineering and rehabilitation·2026
Same author

Retrospective Analysis of Cardiovascular Effects of FES Cycling in People with Complete and Incomplete Spinal Cord Injury.

Journal of clinical medicine·2026
Same author

Stability of performance in a hierarchical system during isometric force production and effects of visual feedback.

Experimental brain research·2025

Area of Science:

  • Motor Control
  • Computational Neuroscience
  • Biomechanics

Background:

  • Understanding how the central nervous system (CNS) organizes redundant motor DoF is crucial for explaining motor learning.
  • The uncontrolled manifold (UCM) approach quantifies the neural control of multi-joint systems by analyzing joint angle variability during task performance.

Purpose of the Study:

  • To investigate the structure of joint angle variability during practice of fast, accurate 3D bimanual pointing movements.
  • To test the bimanual control hypothesis (stabilizing relative endpoint trajectories) against the unimanual control hypothesis (stabilizing individual arm trajectories) using the UCM approach.

Main Methods:

  • Subjects performed bimanual pointing movements in 3D space across pre-test, practice, and post-test sessions.
  • The UCM computational approach was used to calculate variance components (V(COMP), V(UN)) and their ratio (R(V)) across normalized movement time.

Related Experiment Videos

  • R(V) served as a quantitative index of selective stabilization for both bimanual and unimanual control hypotheses.

    • Main Results:

    • Both bimanual and unimanual control hypotheses were supported, with significantly higher R(V) for bimanual control, indicating greater stabilization of relative endpoint trajectories.
    • Despite improvements in movement speed and accuracy with practice, the R(V) index showed no significant changes.
    • Both V(COMP) and V(UN) decreased with practice, maintaining a constant R(V) ratio.

    Conclusions:

    • The CNS prioritizes stabilizing the relative motion between endpoints over stabilizing individual arm trajectories in external space.
    • The UCM approach provides a valuable tool for tracking organizational changes in multi-effector systems during motor learning.
    • Practice enhances performance efficiency without altering the fundamental strategy of stabilizing task-relevant variables.